Multivariant Drone Control using EEG signals Brain Computer Interface based on advanced machine learning tools
This work studied classification of EEG signals used in a study of memory. The goal was to evaluate the performance of the state of the art algorithms. A secondary goal was to try to improve upon the result of a method that was used in a study similar to the one used in this work. For the exper
2025-06-28 16:34:14 - Adil Khan
Multivariant Drone Control using EEG signals Brain Computer Interface based on advanced machine learning tools
Project Area of Specialization Artificial IntelligenceProject SummaryThis work studied classification of EEG signals used in a study of memory. The goal was to evaluate the performance of the state of the art algorithms. A secondary goal was to try to improve upon the result of a method that was used in a study similar to the one used in this work. For the experiment, the signals were transformed into the frequency domain and their magnitudes were used as features. A subset of these features was then selected and fed into a support vector machine classifier. The first part of this work tried to improve the selection of features that was used to discriminate between different memory categories. The second part investigated the uses of time series as features instead of time points.
Project ObjectivesThe main Objective of this project is EEG signal processing and analysis of it. So it includes the following steps:
1. Collection the database (brain signal data).
2. Development of effective algorithm for denoising of EEG signal.
3. Processing the data using effective algorithm.
4. Develop effective algorithm for analyzing the EEG signal in Time-Frequency.
5. Classify EEG signal by frequency analyzing
6. Signal processing and analysis will be done by using MATLAB.
Data Acquisition
Denoising by using Discrete Wavelet Transform
Feature Extraction
Classification by Support Vector Machine
Benefits of the ProjectDrones are very popular because mass media networks patronize its functionality and efficiency when capturing videos and images. You will notice that drones are common in touristy areas due to travel blogger promotions. Video bloggers use drones to further increase the popularity of their videos; hence, promoting the device to other new vloggers. Travel companies use drones to maximize the tourism potential of an area that is popular to all tourists..
Technical Details of Final DeliverableEEG device and applies an eye blink search algorithm to classify the eye blink events in real time. The algorithm takes the amplitude of F8 electrode recording as the feature and classifies eye blink events using Empirical Mode Decomposition, normalizing function and a cut off amplitude level. Every time the system detects two eye blink in a time interval of two seconds it gives take of/land command to a drone which is connected to the BCI system via WiFi and each time it detects one eye blink it gives commands for moving forward and backward.
Final Deliverable of the Project Hardware SystemType of Industry Education , IT Technologies Artificial Intelligence(AI)Sustainable Development Goals No Poverty, Industry, Innovation and InfrastructureRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 65000 | |||
| Arduino uno | Equipment | 1 | 500 | 500 |
| A/D converter | Equipment | 1 | 500 | 500 |
| Emotiv kit | Equipment | 1 | 64000 | 64000 |
| Electrodes | Equipment | 0 | 5000 | 0 |